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A method for opinion mining of product reviews using association rules

Published: 24 November 2009 Publication History

Abstract

When most people buy the products, they inquire about other people's opinion and refer to recommended product. Today, the result of explosive development of the Web makes it easy to consult other people's opinion information. These variety of opinion data are not only useful to customers, but also manufacturers. As a result, opinion mining research to analyze opinion data on the web has become a popular topic recently. In this paper, we proposed opinion mining method for product reviews. In our approach, we first do POS tagging on each review sentence, and we extract feature and opinion words in form of transaction data. Then we discover association rules of needed type from the transaction data, and provide information that is summarized advantages and disadvantages using PMI-IR algorithm.

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cover image ACM Other conferences
ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
November 2009
1479 pages
ISBN:9781605587103
DOI:10.1145/1655925
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 November 2009

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Author Tags

  1. PMI method
  2. POS tagging
  3. association rule mining
  4. opinion mining
  5. opinion summarization

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  • (2022)Sentiment analysis, opinion mining and topic modelling of epics and novels using machine learning techniquesMaterials Today: Proceedings10.1016/j.matpr.2021.06.00151(576-584)Online publication date: 2022
  • (2022)Why are some social-media contents more popular than others? Opinion and association rules mining applied to virality patterns discoveryExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116676197:COnline publication date: 18-May-2022
  • (2022)Mining Association Rules in Commuter Feedback Comments from Facebook of Swiss National Railways (SBB) Using Apriori AlgorithmData and Information in Online Environments10.1007/978-3-031-22324-2_18(230-241)Online publication date: 17-Dec-2022
  • (2019)Study of Socio-Linguistics Online Review System Using Sentiment Scoring MethodIntelligent Computing and Optimization10.1007/978-3-030-33585-4_56(569-580)Online publication date: 27-Oct-2019
  • (2016)Opinion Mining Classification Based on Extension of Opinion Mining PhrasesProceedings of International Conference on ICT for Sustainable Development10.1007/978-981-10-0129-1_74(717-724)Online publication date: 11-Feb-2016
  • (2014)Opinion mining from online user reviews using fuzzy linguistic hedgesApplied Computational Intelligence and Soft Computing10.1155/2014/7359422014(2-2)Online publication date: 1-Jan-2014
  • (2013)Semisupervised learning based opinion summarization and classification for online product reviewsApplied Computational Intelligence and Soft Computing10.1155/2013/9107062013(10-10)Online publication date: 1-Jan-2013
  • (2013)Effectively and efficiently supporting crowd-enabled databases via NoSQL paradigmsProceedings of the 3rd International Workshop on Semantic Search Over the Web10.1145/2509908.2509914(1-5)Online publication date: 30-Aug-2013
  • (2013)Some Remarks on the Internal Consistency of Online Consumer ReviewsAustralasian Marketing Journal10.1016/j.ausmj.2013.08.00121:4(221-227)Online publication date: 1-Nov-2013
  • (2012)Extended Twofold-LDA Model for Two Aspects in One SentenceAdvances in Computational Intelligence10.1007/978-3-642-31715-6_29(265-275)Online publication date: 2012
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